Examining the effectiveness of Quality Outcomes Framework targets using individual level data : an econometric analysis

• Main Author: Hewitt, Neil
• Published: University of Nottingham 2014
• Subjects: 362.1; W Health professions
• Online Access: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.664680

Background: The Quality Outcomes Framework (QOF) was introduced in April 2004. Research to date has primarily looked at its effects on process and surrogate outcomes measured in primary care rather than evidence linked hospital admissions. None has used individual linked data. This study utilises data linkages between the Clinical Practice Research Datalink (CPRD) and Hospital Episode Statistics (HES) datasets to determine the impact of QOF targets on hospital admissions at the individual patient level. Methods: CHD QOF targets and linked hospital admissions were selected according to the strength of their evidence base and ease of extraction. Outcomes were ICD10 codes for Ischaemic Heart Disease (IHD). These formed the primary diagnosis over a hospitalisation and depending on the severity of the code, were additionally an emergency admission. Econometric analysis was then undertaken with IHD admissions as the dependent variable and evidence based CHD QOF targets as explanatory variables. Results: Evidence based CHD QOF targets were found to significantly reduce outcomes after a one year lag. Of the co-morbidities included, only Heart Failure was consistently found to significantly increase outcomes in all analyses. Higher deprivation, and having a study outcome prior to CHD diagnosis, significantly increased outcomes. Being treated in a higher performing practice on the selected targets, in itself, significantly reduced outcomes. Conclusion: This study has demonstrated at the individual level that evidence linked targets in the QOF are effective in reducing linked hospital admissions with a lag. It is the first study to take advantage of CPRD and HES linkages to do so. This has been demonstrated in a ‘real’ world setting, outside of controlled clinical settings, and in so doing addressed deficiencies identified in the existing research. This research has shown that large administrative datasets can support such research and opened up a number of possibilities for future research.